{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "d66580d1-f255-86b9-e5c0-f012342ab8d8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass    ...        Fare Cabin  Embarked\n",
       "0            1         0       3    ...      7.2500   NaN         S\n",
       "1            2         1       1    ...     71.2833   C85         C\n",
       "2            3         1       3    ...      7.9250   NaN         S\n",
       "\n",
       "[3 rows x 12 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Imports needed for the script\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import xgboost as xgb\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls\n",
    "\n",
    "from sklearn import tree\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from IPython.display import Image as PImage\n",
    "from subprocess import check_call\n",
    "from PIL import Image, ImageDraw, ImageFont\n",
    "\n",
    "# Loading the data\n",
    "train = pd.read_csv('../input/train.csv')\n",
    "test = pd.read_csv('../input/test.csv')\n",
    "\n",
    "# Store our test passenger IDs for easy access\n",
    "PassengerId = test['PassengerId']\n",
    "\n",
    "# Showing overview of the train dataset\n",
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "72063fe1-8b92-850b-3ede-5e030c7429ae"
   },
   "outputs": [],
   "source": [
    "# Copy original dataset in case we need it later when digging into interesting features\n",
    "# WARNING: Beware of actually copying the dataframe instead of just referencing it\n",
    "# \"original_train = train\" will create a reference to the train variable (changes in 'train' will apply to 'original_train')\n",
    "original_train = train.copy() # Using 'copy()' allows to clone the dataset, creating a different object with the same values\n",
    "\n",
    "# Feature engineering steps taken from Sina and Anisotropic, with minor changes to avoid warnings\n",
    "full_data = [train, test]\n",
    "\n",
    "# Feature that tells whether a passenger had a cabin on the Titanic\n",
    "train['Has_Cabin'] = train[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\n",
    "test['Has_Cabin'] = test[\"Cabin\"].apply(lambda x: 0 if type(x) == float else 1)\n",
    "\n",
    "# Create new feature FamilySize as a combination of SibSp and Parch\n",
    "for dataset in full_data:\n",
    "    dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1\n",
    "# Create new feature IsAlone from FamilySize\n",
    "for dataset in full_data:\n",
    "    dataset['IsAlone'] = 0\n",
    "    dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1\n",
    "# Remove all NULLS in the Embarked column\n",
    "for dataset in full_data:\n",
    "    dataset['Embarked'] = dataset['Embarked'].fillna('S')\n",
    "# Remove all NULLS in the Fare column\n",
    "for dataset in full_data:\n",
    "    dataset['Fare'] = dataset['Fare'].fillna(train['Fare'].median())\n",
    "\n",
    "# Remove all NULLS in the Age column\n",
    "for dataset in full_data:\n",
    "    age_avg = dataset['Age'].mean()\n",
    "    age_std = dataset['Age'].std()\n",
    "    age_null_count = dataset['Age'].isnull().sum()\n",
    "    age_null_random_list = np.random.randint(age_avg - age_std, age_avg + age_std, size=age_null_count)\n",
    "    # Next line has been improved to avoid warning\n",
    "    dataset.loc[np.isnan(dataset['Age']), 'Age'] = age_null_random_list\n",
    "    dataset['Age'] = dataset['Age'].astype(int)\n",
    "\n",
    "# Define function to extract titles from passenger names\n",
    "def get_title(name):\n",
    "    title_search = re.search(' ([A-Za-z]+)\\.', name)\n",
    "    # If the title exists, extract and return it.\n",
    "    if title_search:\n",
    "        return title_search.group(1)\n",
    "    return \"\"\n",
    "\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Name'].apply(get_title)\n",
    "# Group all non-common titles into one single grouping \"Rare\"\n",
    "for dataset in full_data:\n",
    "    dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')\n",
    "\n",
    "    dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')\n",
    "    dataset['Title'] = dataset['Title'].replace('Mme', 'Mrs')\n",
    "\n",
    "for dataset in full_data:\n",
    "    # Mapping Sex\n",
    "    dataset['Sex'] = dataset['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "    \n",
    "    # Mapping titles\n",
    "    title_mapping = {\"Mr\": 1, \"Master\": 2, \"Mrs\": 3, \"Miss\": 4, \"Rare\": 5}\n",
    "    dataset['Title'] = dataset['Title'].map(title_mapping)\n",
    "    dataset['Title'] = dataset['Title'].fillna(0)\n",
    "\n",
    "    # Mapping Embarked\n",
    "    dataset['Embarked'] = dataset['Embarked'].map( {'S': 0, 'C': 1, 'Q': 2} ).astype(int)\n",
    "    \n",
    "    # Mapping Fare\n",
    "    dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] \t\t\t\t\t\t        = 0\n",
    "    dataset.loc[(dataset['Fare'] > 7.91) & (dataset['Fare'] <= 14.454), 'Fare'] = 1\n",
    "    dataset.loc[(dataset['Fare'] > 14.454) & (dataset['Fare'] <= 31), 'Fare']   = 2\n",
    "    dataset.loc[ dataset['Fare'] > 31, 'Fare'] \t\t\t\t\t\t\t        = 3\n",
    "    dataset['Fare'] = dataset['Fare'].astype(int)\n",
    "    \n",
    "    # Mapping Age\n",
    "    dataset.loc[ dataset['Age'] <= 16, 'Age'] \t\t\t\t\t       = 0\n",
    "    dataset.loc[(dataset['Age'] > 16) & (dataset['Age'] <= 32), 'Age'] = 1\n",
    "    dataset.loc[(dataset['Age'] > 32) & (dataset['Age'] <= 48), 'Age'] = 2\n",
    "    dataset.loc[(dataset['Age'] > 48) & (dataset['Age'] <= 64), 'Age'] = 3\n",
    "    dataset.loc[ dataset['Age'] > 64, 'Age'] ;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "0486bde8-a09f-93c6-832d-c5c9760e2f6e"
   },
   "outputs": [],
   "source": [
    "# Feature selection: remove variables no longer containing relevant information\n",
    "drop_elements = ['PassengerId', 'Name', 'Ticket', 'Cabin', 'SibSp']\n",
    "train = train.drop(drop_elements, axis = 1)\n",
    "test  = test.drop(drop_elements, axis = 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "abef639b-2b31-4209-20ef-d8ba15c4de74"
   },
   "source": [
    "## Visualising processed data ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "b587a7d3-36be-408e-0dee-6fd292e9621b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Has_Cabin</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>IsAlone</th>\n",
       "      <th>Title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Survived  Pclass  Sex  Age  ...    Has_Cabin  FamilySize  IsAlone  Title\n",
       "0         0       3    1    1  ...            0           2        0      1\n",
       "1         1       1    0    2  ...            1           2        0      3\n",
       "2         1       3    0    1  ...            0           1        1      4\n",
       "\n",
       "[3 rows x 11 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "d289fd14-d3a9-6131-3b3a-058edced24f1"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fee18d61978>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colormap = plt.cm.viridis\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.title('Pearson Correlation of Features', y=1.05, size=15)\n",
    "sns.heatmap(train.astype(float).corr(),linewidths=0.1,vmax=1.0, square=True, cmap=colormap, linecolor='white', annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "ce843f51-6f28-ef63-5f21-5f6bf8802e7f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.156673</td>\n",
       "      <td>517</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.575000</td>\n",
       "      <td>40</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.793651</td>\n",
       "      <td>126</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.702703</td>\n",
       "      <td>185</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.347826</td>\n",
       "      <td>23</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Survived           \n",
       "           mean count  sum\n",
       "Title                     \n",
       "1      0.156673   517   81\n",
       "2      0.575000    40   23\n",
       "3      0.793651   126  100\n",
       "4      0.702703   185  130\n",
       "5      0.347826    23    8"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Title', 'Survived']].groupby(['Title'], as_index=False).agg(['mean', 'count', 'sum'])\n",
    "# Since \"Survived\" is a binary class (0 or 1), these metrics grouped by the Title feature represent:\n",
    "    # MEAN: survival rate\n",
    "    # COUNT: total observations\n",
    "    # SUM: people survived\n",
    "\n",
    "# title_mapping = {\"Mr\": 1, \"Miss\": 2, \"Mrs\": 3, \"Master\": 4, \"Rare\": 5} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "05abd10a-b698-dc8a-f745-59436608e38c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Survived</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.742038</td>\n",
       "      <td>314</td>\n",
       "      <td>233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.188908</td>\n",
       "      <td>577</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Survived           \n",
       "         mean count  sum\n",
       "Sex                     \n",
       "0    0.742038   314  233\n",
       "1    0.188908   577  109"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[['Sex', 'Survived']].groupby(['Sex'], as_index=False).agg(['mean', 'count', 'sum'])\n",
    "# Since Survived is a binary feature, this metrics grouped by the Sex feature represent:\n",
    "    # MEAN: survival rate\n",
    "    # COUNT: total observations\n",
    "    # SUM: people survived\n",
    "    \n",
    "# sex_mapping = {{'female': 0, 'male': 1}} "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "a9e03eff-7835-6804-ee13-3cf3f7a80652"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Sex</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Title</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Capt</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Col</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Countess</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Don</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dr</th>\n",
       "      <td>0.857143</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Jonkheer</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lady</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Major</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Master</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Miss</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>182</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mlle</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mme</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mr</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>517</td>\n",
       "      <td>517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mrs</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>125</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ms</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rev</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sir</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Sex           \n",
       "              mean count  sum\n",
       "Title                        \n",
       "Capt      1.000000     1    1\n",
       "Col       1.000000     2    2\n",
       "Countess  0.000000     1    0\n",
       "Don       1.000000     1    1\n",
       "Dr        0.857143     7    6\n",
       "Jonkheer  1.000000     1    1\n",
       "Lady      0.000000     1    0\n",
       "Major     1.000000     2    2\n",
       "Master    1.000000    40   40\n",
       "Miss      0.000000   182    0\n",
       "Mlle      0.000000     2    0\n",
       "Mme       0.000000     1    0\n",
       "Mr        1.000000   517  517\n",
       "Mrs       0.000000   125    0\n",
       "Ms        0.000000     1    0\n",
       "Rev       1.000000     6    6\n",
       "Sir       1.000000     1    1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's use our 'original_train' dataframe to check the sex distribution for each title.\n",
    "# We use copy() again to prevent modifications in out original_train dataset\n",
    "title_and_sex = original_train.copy()[['Name', 'Sex']]\n",
    "\n",
    "# Create 'Title' feature\n",
    "title_and_sex['Title'] = title_and_sex['Name'].apply(get_title)\n",
    "\n",
    "# Map 'Sex' as binary feature\n",
    "title_and_sex['Sex'] = title_and_sex['Sex'].map( {'female': 0, 'male': 1} ).astype(int)\n",
    "\n",
    "# Table with 'Sex' distribution grouped by 'Title'\n",
    "title_and_sex[['Title', 'Sex']].groupby(['Title'], as_index=False).agg(['mean', 'count', 'sum'])\n",
    "\n",
    "# Since Sex is a binary feature, this metrics grouped by the Title feature represent:\n",
    "    # MEAN: percentage of men\n",
    "    # COUNT: total observations\n",
    "    # SUM: number of men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "1efa362d-39b9-a193-a35d-051fdfafb35f"
   },
   "outputs": [],
   "source": [
    "# Define function to calculate Gini Impurity\n",
    "def get_gini_impurity(survived_count, total_count):\n",
    "    survival_prob = survived_count/total_count\n",
    "    not_survival_prob = (1 - survival_prob)\n",
    "    random_observation_survived_prob = survival_prob\n",
    "    random_observation_not_survived_prob = (1 - random_observation_survived_prob)\n",
    "    mislabelling_survided_prob = not_survival_prob * random_observation_survived_prob\n",
    "    mislabelling_not_survided_prob = survival_prob * random_observation_not_survived_prob\n",
    "    gini_impurity = mislabelling_survided_prob + mislabelling_not_survided_prob\n",
    "    return gini_impurity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "d752e767-c0df-3eea-f587-e4d21a09128e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47301295786144265"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini Impurity of starting node\n",
    "gini_impurity_starting_node = get_gini_impurity(342, 891)\n",
    "gini_impurity_starting_node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "cd0f8a03-db86-62c2-c88a-2d54de7531bc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3064437162277843"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini Impurity decrease of node for 'male' observations\n",
    "gini_impurity_men = get_gini_impurity(109, 577)\n",
    "gini_impurity_men"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "183aa17e-27a9-0fae-35e9-34ff2f8f6c6a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3828350034484158"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini Impurity decrease if node splited for 'female' observations\n",
    "gini_impurity_women = get_gini_impurity(233, 314)\n",
    "gini_impurity_women"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "60d97095-3d8a-803a-f649-647cfb6f2965"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.13964795747285214"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini Impurity decrease if node splited by Sex\n",
    "men_weight = 577/891\n",
    "women_weight = 314/891\n",
    "weighted_gini_impurity_sex_split = (gini_impurity_men * men_weight) + (gini_impurity_women * women_weight)\n",
    "\n",
    "sex_gini_decrease = weighted_gini_impurity_sex_split - gini_impurity_starting_node\n",
    "sex_gini_decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_cell_guid": "a8cf9b6c-5299-e522-07d7-cd9f1914afb8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.26425329886377663"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini Impurity decrease of node for observations with Title == 1 == Mr\n",
    "gini_impurity_title_1 = get_gini_impurity(81, 517)\n",
    "gini_impurity_title_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_cell_guid": "b0a7c30e-d8d7-9fdb-747e-1187a158051a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42170207898424317"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini Impurity decrease if node splited for observations with Title != 1 != Mr\n",
    "gini_impurity_title_others = get_gini_impurity(261, 374)\n",
    "gini_impurity_title_others"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_cell_guid": "8dd27088-768a-b1cd-aa51-0c907e066880"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.14267004758907514"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gini Impurity decrease if node splited for observations with Title == 1 == Mr\n",
    "title_1_weight = 517/891\n",
    "title_others_weight = 374/891\n",
    "weighted_gini_impurity_title_split = (gini_impurity_title_1 * title_1_weight) + (gini_impurity_title_others * title_others_weight)\n",
    "\n",
    "title_gini_decrease = weighted_gini_impurity_title_split - gini_impurity_starting_node\n",
    "title_gini_decrease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "33f3999e-e0da-0e2e-dfa1-7be4824c6a36"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max Depth  Average Accuracy\n",
      "        1          0.782285\n",
      "        2          0.799189\n",
      "        3          0.828277\n",
      "        4          0.819288\n",
      "        5          0.815955\n",
      "        6          0.814869\n",
      "        7          0.817079\n",
      "        8          0.809238\n",
      "        9          0.822722\n",
      "       10          0.812609\n"
     ]
    }
   ],
   "source": [
    "cv = KFold(n_splits=10)            # Desired number of Cross Validation folds\n",
    "accuracies = list()\n",
    "max_attributes = len(list(test))\n",
    "depth_range = range(1, max_attributes + 1)\n",
    "\n",
    "# Testing max_depths from 1 to max attributes\n",
    "# Uncomment prints for details about each Cross Validation pass\n",
    "for depth in depth_range:\n",
    "    fold_accuracy = []\n",
    "    tree_model = tree.DecisionTreeClassifier(max_depth = depth)\n",
    "    # print(\"Current max depth: \", depth, \"\\n\")\n",
    "    for train_fold, valid_fold in cv.split(train):\n",
    "        f_train = train.loc[train_fold] # Extract train data with cv indices\n",
    "        f_valid = train.loc[valid_fold] # Extract valid data with cv indices\n",
    "\n",
    "        model = tree_model.fit(X = f_train.drop(['Survived'], axis=1), \n",
    "                               y = f_train[\"Survived\"]) # We fit the model with the fold train data\n",
    "        valid_acc = model.score(X = f_valid.drop(['Survived'], axis=1), \n",
    "                                y = f_valid[\"Survived\"])# We calculate accuracy with the fold validation data\n",
    "        fold_accuracy.append(valid_acc)\n",
    "\n",
    "    avg = sum(fold_accuracy)/len(fold_accuracy)\n",
    "    accuracies.append(avg)\n",
    "    # print(\"Accuracy per fold: \", fold_accuracy, \"\\n\")\n",
    "    # print(\"Average accuracy: \", avg)\n",
    "    # print(\"\\n\")\n",
    "    \n",
    "# Just to show results conveniently\n",
    "df = pd.DataFrame({\"Max Depth\": depth_range, \"Average Accuracy\": accuracies})\n",
    "df = df[[\"Max Depth\", \"Average Accuracy\"]]\n",
    "print(df.to_string(index=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "da2fb75f-cd2c-f3ac-5f83-a0f1ff989080"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.38"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_decision_tree = round(decision_tree.score(x_train, y_train) * 100, 2)\n",
    "acc_decision_tree"
   ]
  }
 ],
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